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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaobo Liu Zhentao Liu Guangjun Wang Zhihua Cai Harry Zhang School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science China University of Geosciences Wuhan China Faculty of Computer Science University of New Brunswick NB Canada

ISBN: (纸本)9781509061839

Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data with heaven weight in the source domain, and the resampled data is used with the target data as the training data to build a classifier. Then the TrAdaBoost algorithm is used to adjust the weights of source data and target data. We discuss Decision Tree, Naive Bayes, and SVM as the base learner in TrResampling, and choose the suitable for TrResampling. In order to illustrate the performance of the proposed algorithm, we compare TrResampling with the state-of-the-art algorithm TrAdaBoost and the base learner Decision Tree, experimental results on UCI data sets indicate that TrResampling is superior to TrAdaBoost and Decision Tree on many data sets.

关键词： Training data Boosting Machine learning algorithms Support vector machines Decision trees Training Electronic mail

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A Camera Self-Calibration Method Based on Parallel QPSO

A Camera Self-Calibration Method Based on Parallel QPSO

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第36届中国控制会议

作者： QIU Huang-bin ZHANG Xin-yu DENG Fang GAO Xin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Radio Film & TV Design and Research InstituteBeijing

In the field of machine vision,camera calibration is a key *** self-calibration,one of camera calibration methods,is only based on the images to calculate the camera’s intrinsic *** has simple calibration process and strong *** self-calibration algorithm needs to calculate the epipole and fundamental matrix by solving the Kruppa equation,but the uncertainty of the epipole always leads to large error and long operation *** improve the precision of camera calibration and reduce the time consumption,the parallel quantum particle swarm algorithm（QPSO） is introduced to solve the improved Kruppa *** can figure out the camera intrinsic parameters and transform the calculation of epipole into the adaptive value of the cost *** with the ordinary particle swarm optimization algorithm（PSO）,QPSO has less parameters,better robustness and faster convergence *** using a multi-core computer platform,its parallel processing has also combined with the characteristics of parallel computing which improves the calculation *** results show that the proposed method is more accurate than ordinary PSO,and the program time consuming is significantly reduced.

关键词： parallel computing quantum particle swarm algorithm（QPSO） camera self-calibration Kruppa equation

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New advances in complex motion control for single robot systems and multi-agent systems

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Science China(Technological Sciences) 2016年第12期59卷 1963-1964页

作者： FANG Hao LU Shao Lei CHEN Jie School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China

CHEN Jie,email：chenjie@***

关键词： robot advances email cooperation simplified decomposed scenarios classic climbing obstacles

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Compressive phase retrieval via reweighted amplitude flow

arXiv

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arXiv 2017年

作者： Zhang, Liang Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

The problem of reconstructing a sparse signal vector from magnitude-only measurements (a.k.a., compressive phase retrieval), emerges naturally in diverse applications, but it is NP-hard in general. Building on recent advances in nonconvex optimization, this paper puts forth a new algorithm that is termed compressive reweighted amplitude flow and abbreviated as CRAF, for compressive phase retrieval. Specifically, CRAF operates in two stages. The first stage seeks a sparse initial guess via a new spectral procedure. In the second stage, CRAF implements a few hard thresholding based iterations using reweighted gradients. When there are sufficient measurements, CRAF provably recovers the underlying signal vector exactly with high probability under suitable conditions. Moreover, its sample complexity coincides with that of the state-of-the-art procedures. Finally, substantial simulated tests showcase remarkable performance of the new spectral initialization, as well as improved exact recovery relative to competing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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Improved Nonsingular Terminal Sliding Mode Attitude Tracking control for Reentry Vehicle 35

Improved Nonsingular Terminal Sliding Mode Attitude Tracking...

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第35届中国控制会议

作者： CHENG Yifeng SHENG Yongzhi LIU Xiangdong School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781509009107

This paper investigates the attitude tracking control problem of reentry vehicle with modeling inaccuracies and external disturbances. An improved nonsingular terminal sliding mode control method is presented, and a faster convergence rate is obtained in comparison with the conventional nonsingular terminal sliding mode control(NTSM). By the proposed control strategy, the possible singularity during the control phase is avoided, and robustness is also guaranteed. Simulation is made for a reentry vehicle in the condition aerodynamic parameters and atmospheric density are perturbed. The results show the effectiveness of the proposed strategies.

关键词： reentry vehicle improved nonsingular terminal sliding mode fast convergence rate Lyapunov stability singularity elimination

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External consensus of networked multi-agent systems with nonlinear dynamics and random network delay

External consensus of networked multi-agent systems with non...

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Asian control Conference

作者： Qi Lei Jia-Hui Li Guo-Ping Liu Min Wu School of Information Science and Engineering University of Central South University Changsha China School of Engineering University of South Wales Pontypridd U.K. School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, the external consensus problem of networked multi-agent systems with fixed undirected topology, random network delay and nonlinear dynamics is considered. The unknown nonlinear dynamics existing in the systems can be described as RBF-ARX model. Besides, the random network delay can be compensated by introducing the prediction strategy. The RBF-ARX model is used as prediction model to design the output predictor, which is to generate the prediction sequence of control input and agent's output. Then, the designed selector chooses the proper value from the available sequences corresponding to the value of the network delay. Based on the mentioned above, a distributed external consensus control algorithm is proposed to make the considered systems with external reference input achieve consensus. Finally, an example is given to illustrate the validity of the proposed algorithm.

关键词： Delays Nonlinear dynamical systems Algorithm design and analysis Prediction algorithms Predictive models Network topology Radial basis function networks

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Disturbance Rejection based on Equivalent-Input-Disturbance Approach for Nonlinear Time-Delay systems

Disturbance Rejection based on Equivalent-Input-Disturbance ...

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Asian control Conference

作者： Fang Gao Min Wu Jinhua She Mingxing Fang Youwu Du Fang Wang College of Physics and Electronic Information Anhui Normal University Wuhu Anhui China School of Automation China University of Geosciences Wuhan Hubei China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhenzhou Henan China

ISBN: (纸本)9781509015740;9781509015733

A new disturbance rejection method to achieves good control performance for a nonlinear time-delay system is presented in this paper. This method effectively reject any disturbances. First, the configuration of the control system is constructed based on the equivalent-input-disturbance method. Then, a sufficient condition for global uniform ultimate boundedness and feedback controller are developed using linear matrix inequality. Finally, the effectiveness of the method is proved by a numerical simulation.

关键词： Matrix decomposition Observers Symmetric matrices Adaptive control Closed loop systems Steady-state

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Distributed computation of linear matrix equations: An optimization perspective

arXiv

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arXiv 2017年

作者： Zeng, Xianlin Liang, Shu Hong, Yiguang Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Beijing100081 China

This paper investigates the distributed computation of the well-known linear matrix equation in the form of AXB = F, with the matrices A, B, X, and F of appropriate dimensions, over multiagent networks from an optimization perspective. In this paper, we consider the standard distributed matrix-information structures, where each agent of the considered multi-agent network has access to one of the sub-block matrices of A, B, and F. To be specific, we first propose different decomposition methods to reformulate the matrix equations in standard structures as distributed constrained optimization problems by introducing substitutional variables;we show that the solutions of the reformulated distributed optimization problems are equivalent to least squares solutions to original matrix equations;and we design distributed continuous-time algorithms for the constrained optimization problems, even by using augmented matrices and a derivative feedback technique. Moreover, we prove the exponential convergence of the algorithms to a least squares solution to the matrix equation for any initial condition. Copyright © 2017, The Authors. All rights reserved.

关键词： Continuous time systems

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Training and testing object detectors with virtual images

arXiv

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arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Adaptive droop control with self-adjusted virtual impedance for three-phase inverter under unbalanced conditions 50

Adaptive droop control with self-adjusted virtual impedance ...

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50th IEEE International Symposium on Circuits and systems, ISCAS 2017

作者： Lu, Zelun Li, Wenxuan Li, Zhen Chen, Xi Lu, Herbert H. C. Dong, Ning Liu, Xiangdong School of Automation Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology BeijingI0008I China Global Energy Interconnection Research Institute North America Santa Clara 5451 Great America Parkway CA95054 United States School of Electrical Electrical and Computer Engineering University of Western Australia CrawleyWA6009 Australia

ISBN: (纸本)9781467368520

Three-phase inverter is a very important interface in microgrid. Droop control and virtual impedance are widely used to improve the power sharing capability and stability of such system, which however becomes ineffective under unbalanced conditions and may even cause potential stability problems. This paper proposes a fully self-adjusted virtual impedance design to guarantee the decoupling of active and reactive power. The arbitrary unbalanced information is acquired through the pseudo-inverse impedance matrix modeled by full-dq technique. Based on the unbalanced impedance identified, the adaptive droop control is achieved for not only accurate power sharing but also reliable voltage support with autonomous unbalanced voltage compensation. Simulations results verify the accurate identification of unbalanced impedance and effectiveness of the proposed method on the unbalanced compensation. © 2017 IEEE.

关键词： Electric inverters

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